Many or most modern industrial and vehicular applications use closed-loop electromechanical components and/or systems. Electromechanical actuators (“EMA”) may be used for positioning a platform of an industrial plant, in robotics contexts, or for any of a wide variety of applications. On board vehicles such as aircraft, it is common for electromechanical actuators to be used to position control surfaces (e.g., for aircraft flight control) and for many other purposes.
Just like any other moving part, electromechanical systems and components degrade during their useful life and eventually wear out. Degradation of mechanical and electromechanical parts can result in increased friction, lower efficiency and eventually, malfunction.
Statistical process control (SPC) and multivariate statistical process control (MSPC) and other statistical methods have been used to monitor electromechanical components in chemical and manufacturing process plants. For example, it is known to use multivariate statistical process control for monitoring a manufacturing process. Non-linear statistical techniques can be used to provide early warning of abnormal situations.
It is generally known to monitor the health of actuators in mechanical and electromechanical systems in order to predict failures and system degradation. For example, it is known to use a model-based method for monitoring the mechanical components of a manufacturing process. Degradation of mechanical sub-systems or components can lead to increased friction, loss of efficiency or other phenomena. Such degradation may build up to the point of causing a system or component jam or other failure.
When mechanical sub-systems or components are controlled in a closed loop control system, the controller is often able to compensate for some level of degradation. In such cases when the controller can compensate, no indication of the degradation can generally be obtained from the outputs of the system—in part because the controller has already compensated for the situation just as it was programmed to do. The controller increases or otherwise changes the command current or voltage in order to deal with the higher friction or other changing factor as the control system adapts automatically to the change in behavioral characteristics of the electromechanical system or component.
The monitoring of this command current or other variables related to the electrical input or control power may indicate abnormalities of electro-mechanical systems or components. Exemplary non-limiting implementations herein use command current or other measurements related to the electrical power or other control parameters to monitor system or component health. In cases where the degradation is more severe, the controller may not be able to compensate and guarantee the performance of the system or component. In this case, output variables of the system, such as positions or speeds, may provide indications of the degradation. The exemplary illustrative non-limiting implementation may also use position or speed measurements to monitor system or component health.
Many systems that are already developed today were not designed to provide ideal information for heath monitoring purposes. In many cases, useful signals may be mixed with others, making useful signal acquisition more difficult. The development of health monitoring systems using these inadequate information is a major challenge.
Blind source separation is a set of statistical techniques that has been used in many different applications for separating independent signals. One of these techniques is called Independent Components Analysis (“ICA”). A major goal of ICA is to separate independent signals that generated a set of combined signals. For example, suppose you are in a room where two people are talking simultaneously and two microphones are recording both voices at different positions in the room. Using ICA, it is possible to extract the voices of the two people separately just using two recordings of both talking at the same time. In industrial applications, it is possible to use ICA to separate influences of different signals in a set of given measurements.
Operational conditions and disturbances which are not related to the system or component degradation may yield increments of the command currents (electrical power) or otherwise change controller output parameters. For reliable monitoring, such operational conditions and disturbances can also be monitored and taken into account. The exemplary illustrative non-limiting implementation may use operational conditions and disturbances measurements as part of an analysis to monitor system or component health.
One exemplary illustrative non-limiting implementation herein uses analytical techniques to process data obtained from sensors that measure quantities related to electromechanical system or component actuation. Such quantities can include, for example, position, speed, command current, command power, or other measurable parameters. Sensors may be used to measure quantities related to the operational conditions of such electromechanical system or component actuation, including but not limited to for example the weight of a platform, the dynamic pressure acting upon an aircraft control surface, or other parameters.
In one exemplary illustrative non-limiting implementation, a method of monitoring the health state of an electromechanical system or component controlled in a closed loop fashion may include collecting a group of measurements of at least one measurable varying parameter from healthy instances of said system or component while it is being commanded for a known pattern of operation under various normal operational conditions. Such collected measurements may be used to construct a statistical model. New measurements collected from the same measurable varying parameters for at least one instance of the system or component can be compared with the statistical model to produce an index of quantitative degradation. An indication may be generated upon detection of an abnormal situation whenever the degradation exceeds an alarm threshold for example. Alternatively, this index can be combined with historical data and this information can be used to predict future health states of the system or component.
In exemplary illustrative non-limiting implementations, measurements can include for example electrical input power, electrical command current to motors, position indicators, speed indication, operational conditions, disturbances or other measurable information.
The predetermined pattern of operation can be a fixed pattern that is the consequence of normal operation. It could also be a pattern based upon testing operations.
The degradation index can be calculated using various calculations including but not limited to Mahalanobis distance, Hotelling's T2, Runger U2, multi-way PCA and/or other analysis.
In one particular exemplary illustrative non-limiting implementation, said electro-mechanical system could be an aircraft flap, slat or other aircraft control surface or actuator.